Following the categorization in the survey on interpreting inner structures of AI models [1]}, the utilization measurements can be related to concept vectors whose goal is to associate directions in latent space with meaningful concepts. In the work of Fong and Vedaldi [2]}(Network 2 Vector) and Bau et al. [3]} (Network Dissection), the distribution of activation maps at each convolutional unit as inputs pass through is used to determine a threshold. Threshold-based segmented activation maps are compared across concepts. In contrast to the previous work [2]},[3]}, our utilization measurements are computed at all computation units in an AI model, the activation maps are binarized at zero, and statistics are computed over a distribution of tensor-states (including the binarized activation maps from convolutional units). Our approach does not use any inserted modules like in concept whitening [6]} to align the latent space with concepts. Furthermore, our approach does not project class activation maps to create saliency maps [7]}, [8]} in the input spatial domain, but, rather, it analyzes class activations in the tensor-state space.
